A growing number of datasets characterize tumors with quantitative proteomic and genomic data. These emerging datasets are typically specific to a single cancer type. However, analysis of proteogenomic patterns across multiple cancer types allows us to identify common mechanisms in the function of cancer, as well as mechanisms that are unique to specific cancer types. Here we present how a Python data API from the NCI's CPTAC program bridges the gap between different cancer datasets to facilitate pancancer analysis. While we have previously presented this API as a general model for data dissemination, we have yet to display the specific pancancer applications of the tool. Several key aspects of its design optimize its use from a pancancer point of view, including parsing data to a single consistent format for all datasets, and automatically managed data updates for each dataset. Our analyses demonstrate the efficacy of our data model for generalizing an analysis pipeline, applying it to multiple cancer types, and comparing the results. This enables unique discoveries that characterize the overall proteogenomic landscape of cancer. Citation Format: Caleb M. Lindgren, Chelsie Minor, Lindsey K. Olsen, Brittany Henderson, CPTAC Investigators, Samuel H. Payne. Data distribution for easy pancancer analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 251.